
"""simple docstring"""






import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						List[str]:

	'''simple docstring'''




	lowerCAmelCase												=  [
	    '''encoder.version''',
	    '''decoder.version''',
	    '''model.encoder.version''',
	    '''model.decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''encoder.embed_positions._float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_lowerCamelCase,					_lowerCamelCase			)




def __magic_name__	(				_lowerCamelCase:		str			)					->						Tuple:

	'''simple docstring'''




	lowerCAmelCase							,       lowerCAmelCase												=  emb.weight.shape
	lowerCAmelCase												=  nn.Linear(_lowerCamelCase,					_lowerCamelCase,					bias=_lowerCamelCase			)
	lowerCAmelCase												=  emb.weight.data
	return lin_layer




def __magic_name__	(				_lowerCamelCase:		List[str],					_lowerCamelCase:		Optional[Any]=None			)					->						Tuple:

	'''simple docstring'''




	lowerCAmelCase												=  {}
	for old_key in state_dict.keys():
		lowerCAmelCase												=  old_key
		if "moe_layer.experts." in key:
			if expert_idx is not None:
				lowerCAmelCase												=  key.replace('''moe_layer.experts.0''',					F"""ffn.experts.expert_{expert_idx}"""			)
			else:
				lowerCAmelCase												=  key.replace('''moe_layer.experts.''',					'''ffn.experts.expert_'''			)
		if "gate" in key:
			lowerCAmelCase												=  key.replace('''.moe_layer.gate.wg''',					'''.ffn.router.classifier'''			)
		if "fc2" and "experts" not in key:
			lowerCAmelCase												=  key.replace('''.fc2.''',					'''.ffn.fc2.'''			)
		if "fc1" and "experts" not in key:
			lowerCAmelCase												=  key.replace('''.fc1.''',					'''.ffn.fc1.'''			)
		if ".encoder_attn." in key:
			lowerCAmelCase												=  key.replace('''.encoder_attn.''',					'''.cross_attention.'''			)
		if "encoder_attn_layer_norm" in key:
			lowerCAmelCase												=  key.replace('''encoder_attn_layer_norm''',					'''cross_attention_layer_norm'''			)
		if "final_layer_norm" in key:
			lowerCAmelCase												=  key.replace('''final_layer_norm''',					'''ff_layer_norm'''			)
		lowerCAmelCase												=  state_dict[old_key]
	return new_dict




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		str,					_lowerCamelCase:		Optional[int],					_lowerCamelCase:		Any,					_lowerCamelCase:		str = WEIGHTS_NAME			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  0
	os.makedirs(_lowerCamelCase,					exist_ok=_lowerCamelCase			)

	for expert in range(_lowerCamelCase			):
		lowerCAmelCase												=  switch_checkpoint_path + F"""-rank-{expert}.pt"""
		if os.path.isfile(_lowerCamelCase			):
			lowerCAmelCase												=  torch.load(_lowerCamelCase			)['''model''']
			remove_ignore_keys_(_lowerCamelCase			)
			lowerCAmelCase												=  rename_fairseq_keys(_lowerCamelCase,					_lowerCamelCase			)
			lowerCAmelCase												=  os.path.join(
			    _lowerCamelCase,					weights_name.replace('''.bin''',					F"""-{len(_lowerCamelCase			)+1:05d}-of-???.bin"""			)			)
			torch.save(_lowerCamelCase,					_lowerCamelCase			)
			sharded_state_dicts.append(expert_state.keys()			)
			total_size += sum([value.numel() for key, value in expert_state.items()]			) * dtype_byte_size(
			    expert_state[list(_lowerCamelCase			)[0]].dtype			)

    # Add the last block
	lowerCAmelCase												=  os.path.join(_lowerCamelCase,					weights_name.replace('''.bin''',					F"""-{len(_lowerCamelCase			)+1:05d}-of-???.bin"""			)			)
	lowerCAmelCase												=  torch.load(switch_checkpoint_path + '''-shared.pt'''			)['''model''']
	remove_ignore_keys_(_lowerCamelCase			)
	lowerCAmelCase												=  rename_fairseq_keys(_lowerCamelCase,					_lowerCamelCase			)
	lowerCAmelCase												=  shared_weights['''decoder.embed_tokens.weight''']
	sharded_state_dicts.append(shared_weights.keys()			)

	# If we only have the shared weights (dummy model/experts saved on the same file)
	if len(_lowerCamelCase			) == 1:
		lowerCAmelCase												=  os.path.join(_lowerCamelCase,					_lowerCamelCase			)
		torch.save(_lowerCamelCase,					_lowerCamelCase			)
		return {weights_name: sharded_state_dicts[0]}, None
	else:
		torch.save(_lowerCamelCase,					_lowerCamelCase			)
	# Otherwise, let's build the index
	lowerCAmelCase												=  {}
	for idx, shard in enumerate(_lowerCamelCase			):
		lowerCAmelCase												=  weights_name.replace('''.bin''',					F"""-{idx+1:05d}-of-{len(_lowerCamelCase			):05d}.bin"""			)
		lowerCAmelCase												=  os.path.join(_lowerCamelCase,					weights_name.replace('''.bin''',					F"""-{idx+1:05d}-of-???.bin"""			)			)
		os.rename(_lowerCamelCase,					os.path.join(_lowerCamelCase,					_lowerCamelCase			)			)
		for key in shard:
			lowerCAmelCase												=  shard_file

    # Add the metadata
	lowerCAmelCase												=  {'''total_size''': total_size}
	lowerCAmelCase												=  {'''metadata''': metadata, '''weight_map''': weight_map}

	with open(os.path.join(_lowerCamelCase,					_lowerCamelCase			),					'''w''',					encoding='''utf-8'''			) as f:
		lowerCAmelCase												=  json.dumps(_lowerCamelCase,					indent=2,					sort_keys=_lowerCamelCase			) + '''\n'''
		f.write(_lowerCamelCase			)

	return metadata, index


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--nllb_moe_checkpoint_path""",
					    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
					    type=str,
					    required=False,
					    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
					)
					parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
					    type=str,
					    required=False,
					    help="""Path to the output pytorch model.""",
					)
					UpperCAmelCase        =				parser.parse_args()
					UpperCAmelCase     ,		UpperCAmelCase        =				shard_on_the_fly(
					    args.nllb_moe_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    1_2_8,
					    args.dtype,
					)

					UpperCAmelCase        =				NllbMoeConfig.from_pretrained(
					    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
					)
					config.save_pretrained(args.pytorch_dump_folder_path)
					UpperCAmelCase        =				NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
					print("""Done""")
					model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""






class 			lowercase					:






			def __init__(self  :	Dict			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  {}






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	None:





				"""simple docstring"""


				print(self.vertex			)
				for i in self.vertex:
					print(SCREAMING_SNAKE_CASE_      ,''' -> '''      ,''' -> '''.join([str(SCREAMING_SNAKE_CASE_			) for j in self.vertex[i]]			)			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	None:





				"""simple docstring"""


				if from_vertex in self.vertex:
					self.vertex[from_vertex].append(SCREAMING_SNAKE_CASE_			)
				else:
					# else make a new vertex
					lowerCAmelCase												=  [to_vertex]






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  [False] * len(self.vertex			)

				# call the recursive helper function
				for i in range(len(self.vertex			)			):
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	list			)       ->	None:





				"""simple docstring"""


				lowerCAmelCase												=  True

				print(SCREAMING_SNAKE_CASE_      ,end=''' '''			)

				# Recur for all the vertices that are adjacent to this node
				for i in self.vertex:
					if not visited[i]:
						self.dfs_recursive(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
					UpperCAmelCase        =				Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print("""DFS:""")
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3


